def renderIndividualHist(self, painting):
height = 500
width = 500
(r, g, b) = cv.GetDims(painting.data.bins)
(_, tallest, _, _) = cv.GetMinMaxHistValue(painting.data)
scale = height / tallest
if scale > 1:
scale = 1
histImage = cv.CreateImage((width, height), 8, 3)
prev = -1
cv.Rectangle(histImage, (0, 0), (width, height), cv.CV_RGB(0, 0, 0),
cv.CV_FILLED)
step = width / (r * g * b)
cur = 0
for (i, j, k) in iter3D(range(r), range(g), range(b)):
intensity = cv.QueryHistValue_3D(painting.data, i, j, k)
color = cv.CV_RGB(int((255 / r) * i), int((255 / g) * j),
int((255 / b) * k))
x1 = cur
cur += step
x2 = x1 + step
cv.Rectangle(histImage, (int(x1), height),
(int(x2), height - int(intensity * scale)), color,
cv.CV_FILLED)
windowTitle = '{0} ({1})'.format(painting.title, painting.year)
cv.NamedWindow(windowTitle)
cv.ShowImage(windowTitle, histImage)
cv.WaitKey(0)
def hue_histogram_as_image(hist):
histimg_hsv = cv.CreateImage((640, 480), 8, 3)
mybins = cv.CloneMatND(hist.bins)
cv.Log(mybins, mybins)
(_, hi, _, _) = cv.MinMaxLoc(mybins)
cv.ConvertScale(mybins, mybins, 255. / hi)
w, h = cv.GetSize(histimg_hsv)
hdims = int(cv.GetDims(mybins)[0])
for x in range(w):
xh = (180 * x) / (w - 1) # hue sweeps from 0-180 across the image
val = int(mybins[int(hdims * x / w)] * h / 255)
cv.Rectangle(histimg_hsv, (x, 0), (x, h - val), (xh, 255, 64), -1)
cv.Rectangle(histimg_hsv, (x, h - val), (x, h), (xh, 255, 255), -1)
histimg = cv.CreateImage((320, 200), 8, 3)
cv.CvtColor(histimg_hsv, histimg, cv.CV_HSV2BGR)
return histimg
def setup_camshift(roi_selection, img):
hist = cv.CreateHist([180], cv.CV_HIST_ARRAY, [(0, 180)], 1)
# backproject_mode = False
histimg_hsv = cv.CreateImage((320, 200), 8, 3)
mybins = cv.CloneMatND(hist.bins)
cv.Log(mybins, mybins)
(_, hi, _, _) = cv.MinMaxLoc(mybins)
cv.ConvertScale(mybins, mybins, 255. / hi)
w, h = cv.GetSize(histimg_hsv)
hdims = cv.GetDims(mybins)[0]
for x in range(w):
xh = (180 * x) / (w - 1) # hue sweeps from 0-180 across the image
val = int(mybins[int(hdims * x / w)] * h / 255)
cv.Rectangle(histimg_hsv, (x, 0), (x, h - val), (xh, 255, 64), -1)
cv.Rectangle(histimg_hsv, (x, h - val), (x, h), (xh, 255, 255), -1)
histimg = cv.CreateImage((320, 200), 8, 3)
cv.CvtColor(histimg_hsv, histimg, cv.CV_HSV2BGR)
# making_selection
# print roi_selection[0]
# print roi_selection[1]
point1 = roi_selection[0]
point2 = roi_selection[1]
xmin = point1[0]
ymin = point1[1]
xmax = point2[0]
ymax = point2[1]
widthx = xmax - xmin
heighty = ymax - ymin
selection = (xmin, ymin, widthx, heighty)
return selection, hist
def calc_1dhisto(inframe,
nbin=256,
scale=2,
histh=200,
hist=None,
histimg=None):
"""
Calculate 1D intensity histogram of a iplimage, and return the histogram
(as cv2.cv.cvhistogram) and an image representing this histogram (as
8bit unsigned iplimage) Use **hist** and **histimg** if they are
provided, otherwise create them from scratch.
To re-use the allocated memory, simply pass the output as input for
input **hist** and **histimg**.
Histogram bar height is calculated as follows:
bin_height = int( bin_count*1.0 / (npix/nbin) * 0.2 * histh )
where bin_height is in pixels, bin_count is the number of pixels in this
bin, npix the total number of pixels in the image, nbin the total bins,
such that (npix/nbin) is the average bin count. 0.2 is a factor that
sets the average bin height to 20% and histh scales the bin normalized
bin height to pixels.
@param [in] inframe Input frame, as iplimage
@param [in] nbin Number of intensity bins
@param [in] scale Histogram image bar width in pixels
@param [in] histh Histogram image height in pixels
@param [in] hist Previously allocated cv2.cv.cvhistogram to use
@param [in] histimg Previously allocated iplimage to use
@return Tuple of (histogram, histogram image)
"""
if (inframe.depth == cv.IPL_DEPTH_32F):
hranges = [[0, 1]]
elif (inframe.depth in [
cv.IPL_DEPTH_8U, cv.IPL_DEPTH_8S, cv.IPL_DEPTH_16U,
cv.IPL_DEPTH_16S, cv.IPL_DEPTH_32S
]):
hranges = None
else:
raise ValueError("Unsupported datatype for histogram ('%s')" %
(str(inframe.depth)))
if (not hist):
hist = cv.CreateHist([nbin],
cv.CV_HIST_ARRAY,
ranges=[[0, 1]],
uniform=1)
if (not histimg):
histimg = cv.CreateImage((nbin * scale, histh), cv.IPL_DEPTH_8U, 1)
cv.CalcHist([cv.GetImage(inframe)], hist)
#hmin, hmax, __, __ = cv.GetMinMaxHistValue(hist)
# White noise histogram height should be be 0.2
npix = np.product(cv.GetDims(inframe))
histogram = [
cv.QueryHistValue_1D(hist, i) * 1.0 / (npix / nbin) * 0.2 * histh
for i in range(nbin)
]
histimg = plot_1dhisto(histogram,
scale=scale,
histh=histh,
histimg=histimg)
return hist, histimg
CAM_CFG = {
'camidx': 0,
'frameidx': 0,
'handler': None,
'buf': [None] * 10,
'roi': roi
}
# Initialize camera handler
CAM_CFG['handler'] = cv.CaptureFromCAM(CAM_CFG['camidx'])
rawframe = cv.QueryFrame(CAM_CFG['handler'])
frame = cv.GetSubRect(rawframe, roi)
# Make fake calibration frames from data
darkarr = (np.random.random(cv.GetDims(frame)) * 10.0 / 256).astype(np.float32)
darkframe = array2cv(darkarr)
lastframe = cv.CloneImage(darkframe)
camframe = cv.CloneImage(darkframe)
diffframe = cv.CloneImage(darkframe)
# Make real flat field
print "Taking 100 flats..."
frame = cv.GetSubRect(cv.QueryFrame(CAM_CFG['handler']), CAM_CFG['roi'])
cv.ConvertScale(frame, camframe, scale=1.0 / 256)
flatframe = cv.CloneImage(camframe)
for i in xrange(9):
print ".",
frame = cv.GetSubRect(cv.QueryFrame(CAM_CFG['handler']), CAM_CFG['roi'])
